Many projection systems will render images for projection, onto a screen and/or an object, based on properties of a projector projecting the images, and it is generally assumed that the projector includes a planar lens to project the images and/or planar projecting occurs when projecting the images. However, non-planar lenses, such as fisheye lenses, can be used with projectors, for wide field-of-view applications, and the like. Due to the additional degrees of freedom involved and because system of equations for an initialization of such modelling is not inherently linear modeling such non-planar lenses and/or non-planar projecting introduces technical challenges. In particular, modeling using random sample consensus (RANSAC) and/or polynomial techniques may not produce a suitable quality of alignment when projecting onto a screen and/or an object using such non-planar lenses and/or non-planar projecting. For example, such techniques can fail on at least two grounds: geometry-modeling approaches assume planar projection, which is not applicable to all lens types; and more naïve mathematical approaches fail to achieve the required accuracy levels, particularly for more complex screen geometry. Furthermore, cameras can also include non-planar lenses, and hence modelling of such cameras can suffer the same problems as modelling projectors that include non-planar lenses; for example, such cameras can be used with projections systems to provide feedback on images projected onto screens, objects, and the like, for example in projection mapping scenarios.